scholarly journals Online NARMAX model for electron fluxes at GEO

2015 ◽  
Vol 33 (3) ◽  
pp. 405-411 ◽  
Author(s):  
R. J. Boynton ◽  
M. A. Balikhin ◽  
S. A. Billings
Keyword(s):  
Electron Flux ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Earth Orbit ◽  
Output Data ◽  
Satellite Systems ◽  
Single Output ◽  
Average Electron ◽  
Solar Wind Parameters ◽  
Nonlinear Autoregressive

Abstract. Multi-input single-output (MISO) nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been derived to forecast the > 0.8 MeV and > 2 MeV electron fluxes at geostationary Earth orbit (GEO). The NARMAX algorithm is able to identify mathematical model for a wide class of nonlinear systems from input–output data. The models employ solar wind parameters as inputs to provide an estimate of the average electron flux for the following day, i.e. the 1-day forecast. The identified models are shown to provide a reliable forecast for both > 0.8 and > 2 MeV electron fluxes and are capable of providing real-time warnings of when the electron fluxes will be dangerously high for satellite systems. These models, named SNB3GEO > 0.8 and > 2 MeV electron flux models, have been implemented online at http://www.ssg.group.shef.ac.uk/USSW/UOSSW.html.

scholarly journals Application of nonlinear autoregressive moving average exogenous input models to geospace: advances in understanding and space weather forecasts

2013 ◽  
Vol 31 (9) ◽  
pp. 1579-1589 ◽  
Author(s):  
R. J. Boynton ◽  
M. A. Balikhin ◽  
S. A. Billings ◽  
O. A. Amariutei
Keyword(s):  
First Principles ◽  
Radiation Belt ◽  
Electron Flux ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Weather Forecasts ◽  
Dst Index ◽  
Identification Technique ◽  
Nonlinear Autoregressive ◽  
Insight Into

Abstract. The nonlinear autoregressive moving average with exogenous inputs (NARMAX) system identification technique is applied to various aspects of the magnetospheres dynamics. It is shown, from an example system, how the inputs to a system can be found from the error reduction ratio (ERR) analysis, a key concept of the NARMAX approach. The application of the NARMAX approach to the Dst (disturbance storm time) index and the electron fluxes at geostationary Earth orbit (GEO) are reviewed, revealing new insight into the physics of the system. The review of studies into the Dst index illustrate how the NARMAX approach is able to find a coupling function for the Dst index from data, which was then analytically justified from first principles. While the review of the electron flux demonstrates how NARMAX is able to reveal new insight into the physics of the acceleration and loss processes within the radiation belt.

A real time forecast of the electron fluxes measured by GOES 16

2021 ◽  
Author(s):  
Richard Boynton ◽  
Michael Balikhin ◽  
Hualiang Wei
Keyword(s):  
Solar Wind ◽  
Real Time ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Time System ◽  
Real Time System ◽  
Forecast Models ◽  
Forecasting System ◽  
Solar Wind Parameters ◽  
Nonlinear Autoregressive

<p>A real time system is developed to forecast the electron fluxes measured by GOES R spacecraft. Forecast models are developed using the system identification/machine learning methodology based on Nonlinear Autoregressive Moving Average exogenous (NARMAX) models. NARMAX algorithms use past input-output data to automatically deduce a model of the system. Here, the solar wind parameters are used as inputs and the electron fluxes measured by GOES 16 are used as the outputs to deduce the models. The models are then implemented in a real time forecasting system. The forecasting system uses real time solar wind data from ACE, DSCOVR, and ENLIL, which are then processed into the correct format for the NARMAX models to provide a forecast of the electron fluxes at geostationary orbit. </p>

scholarly journals A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Nannan Yu ◽  
Lingling Wu ◽  
Dexuan Zou ◽  
Ying Chen ◽  
Hanbing Lu
Keyword(s):  
Evoked Potential ◽  
Moving Average ◽  
Extraction Process ◽  
Autoregressive Moving Average ◽  
Single Trial ◽  
Model Driven ◽  
Single Output ◽  
Multiple Input ◽  
Novel Method ◽  
Electroencephalographic Activity

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method.

scholarly journals ROBUST RECURSIVE IDENTIFICATION OF HAMMERSTEIN MODELS BASED ON WEISZFALD ALGORITHM

2020 ◽  
Vol 18 (2) ◽  
pp. 127
Author(s):  
Vojislav Filipović
Keyword(s):  
Robust Statistics ◽  
Moving Average ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Weighted Norm ◽  
Stochastic Disturbance ◽  
Hammerstein Models ◽  
Non Gaussian ◽  
Weighting Coefficients ◽  
Nonlinear Autoregressive

The Hammerstein models can accurately describe a wide variety of nonlinear systems (chemical process, power electronics, electrical drives, sticky control valves). Algorithms of identification depend, among other, on the assumption about the nature of stochastic disturbance. Practical research shows that disturbances, owing the presence of outliers, have a non-Gaussian distribution. In such case it is a common practice to use the robust statistics. In the paper, by analysis of the least favourable probability density, it is shown that the robust (Huber`s) estimation criterion can be presented as a sum of non-overlapping - norm and - norm criteria. By using a Weiszfald algorithm - norm criterion is converted to - norm criterion. So, the weighted - norm criterion is obtained for the identification. The main contributions of the paper are: (i) Presentation of the Huber`s criterion as a sum of - norm and - norm criteria; (ii) Using the Weiszfald algorithm  – norm criterion is converted to a weighted - norm criterion; (iii) Weighted extended least squares in which robustness is included through weighting coefficients are derived for NARMAX (nonlinear autoregressive moving average with exogenous variable) . The illustration of the behaviour of the proposed algorithm is presented through simulations.

scholarly journals A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Za'er Abo-Hammour ◽  
Othman Alsmadi ◽  
Shaher Momani ◽  
Omar Abu Arqub
Keyword(s):  
Genetic Algorithm ◽  
Dynamical Systems ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Input Output ◽  
Data Sequence ◽  
Output Data ◽  
Linear Dynamical Systems ◽  
Genetic Algorithm Approach

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.

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